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source("./tianfengRwrappers.R")

载入需要的程辑包：dplyr

载入程辑包：‘dplyr’

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载入需要的程辑包：reticulate
载入需要的程辑包：tidyr

载入程辑包：‘MySeuratWrappers’

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NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
Registering fonts with R

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载入需要的程辑包：Biobase
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载入程辑包：‘BiocGenerics’

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    eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match,
    mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames,
    sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

载入需要的程辑包：e1071

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umap图

Idents(CA_dataset2) <- CA_dataset2$conditions
umap_AC <- umapplot(subset(CA_dataset2, idents = "AC"), group.by = "Classification1")
umap_PA <- umapplot(subset(CA_dataset2, idents = "PA"), group.by = "Classification1")

ggsave("umap_AC.svg",device = svg,width = 6,height = 4,plot = umap_AC)
ggsave("umap_PA.svg",device = svg,width = 6,height = 4,plot = umap_PA)

## fig.2
ggsave("ds2_PAumap.svg",plot = umapplot(ds2_PA),device = svg, width = 6, height = 5)
ggsave("ds2_ACumap.svg",plot = umapplot(ds2_AC),device = svg, width = 6, height = 5)

分裂小提琴图

热图 top30的marker，展示其中的部分

Idents(CA_dataset2) <- CA_dataset2$Classification1
CA_dataset2_markers <- FindAllMarkers(CA_dataset2, logfc.threshold = 1, min.diff.pct = 0.3)
top30genes <- CA_dataset2_markers[CA_dataset2_markers$pct.1>0.7,] %>% group_by(cluster) %>% slice_max(n = 30, order_by = avg_logFC)

library(ComplexHeatmap)
##提取标准化表达矩阵
mat <- GetAssayData(CA_dataset2, slot = "scale.data")

gene_show <- c("LYZ","CD68","SPP1","PECAM1","VWF","PLVAP","MYL9","TAGLN","ACTA2","DCN","APOD","FBLN1","CD69","CD79A","MS4A1","TPSAB1","TPSB2","NKG7","STMN1","CENPF","CPA3","IL7R","TRAC")

##获得基因和细胞聚类信息
cluster_info <- sort(CA_dataset2$Classification1)
condition_info <- sort(CA_dataset2$conditions)
heatmapgenes <- intersect(top30genes$gene,rownames(mat))

##筛选矩阵
mat <- as.matrix(mat[heatmapgenes,names(cluster_info)])

#获得基因在热图中的位置信息
gene_pos <- match(gene_show, rownames(mat))
row_anno <- rowAnnotation(gene=anno_mark(at=gene_pos,labels = gene_show))

col <- colors_list
names(col) <- levels(cluster_info)
top_anno <- HeatmapAnnotation(cluster=anno_block(gp=gpar(fill=col),labels = levels(cluster_info),
 labels_gp = gpar(cex=1,col='black')))

library(circlize)
col_fun <-  colorRamp2(c(-2, 1, 4), c("#1E90FF", "white", "#ff2121"))

svg("CA_dataset2_markers.svg",height = 6,width = 10)
Heatmap(mat, cluster_rows = FALSE, cluster_columns = FALSE, 
        show_column_names = FALSE, show_row_names = FALSE,
 column_split = cluster_info, top_annotation = top_anno,  
 column_title = NULL, right_annotation = row_anno, 
 heatmap_legend_param = list(
 title='Expression', title_position='leftcenter-rot'), col = col_fun)
dev.off()

比例

Idents(CA_dataset2) <- CA_dataset2$conditions
AC <- subset(CA_dataset2, idents = "AC")
PA <- subset(CA_dataset2, idents = "PA")
prop_mat <- cbind(prop.table(table(AC$Classification1)),prop.table(table(PA$Classification1)))
colnames(prop_mat) <- c("AC","PA")


plot_data = melt(prop_mat)
colnames(plot_data) = c('cell type','position','proportion')#修改每一列的名称

ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.5) + theme_bw()

prop_plot <- ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "stack", width=0.5) + coord_cartesian(ylim = c(0,0.5))+
  theme_bw() + scale_y_continuous(expand = c(0,0)) + theme(panel.grid = element_blank())

ggsave("CAdataset2_prop.svg",device = svg, plot = prop_plot,width = 4,height = 3)

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